Analytics Engineer

As an Analytics Engineer at our company, you will design and build data pipelines, develop a semantic layer for business metrics, and implement tools like SQLMesh for transformation workflows. Additionally, you will collaborate closely with business stakeholders to assess their reporting needs and ensure data governance and quality.

We are looking for candidates with at least three years of experience in Analytics Engineering, Data Engineering, or BI Engineering. A strong command of SQL, experience with data modelling, and familiarity with tools such as dbt, SQLMesh, or Cube are crucial for this role.

In this position, you will work with various tools such as dbt for transformation workflows, Cube for querying metrics, and SQLMesh for data versioning. Familiarity with data warehouses like Redshift, BigQuery, or Snowflake will also be an integral part of your role.

The ideal candidate for our Analytics Engineer role should possess strong SQL skills, experience in data modelling (Star/Snowflake schema), and knowledge of data governance practices. Familiarity with ETL/ELT processes and workflow orchestration tools like Airflow or Dagster is also beneficial.

We foster a collaborative and innovative culture where our Analytics Engineers work closely with stakeholders to derive actionable insights from data. We value open communication, a passion for data, and creativity in tackling challenges that enhance our data systems.

In preparing for your interview, reflect on specific projects where you used SQL to extract, transform, and analyze data. Discuss the complexity of SQL queries you’ve written and how they helped solve business problems or improve reporting efficiency.

Be ready to clarify that ETL (Extract, Transform, Load) processes data before it's stored, whereas ELT (Extract, Load, Transform) loads raw data into the warehouse first and transforms it later. Provide examples from your experience where applicable.

Discuss how a semantic layer provides a consistent and business-friendly view of data metrics, which can simplify reporting and decision-making. Share experiences where you developed or utilized a semantic layer to improve data accessibility.

Choose a specific project that emphasizes your ability to overcome obstacles. Talk about the complexity of the data sources, the technologies used, and how your decisions improved the efficiency of the pipeline.

Prepare to discuss the practices you follow to ensure data quality, such as implementing tests, monitoring tools, and documentation. Highlight any specific strategies you’ve successfully used to uphold data governance standards.

Talk about your hands-on experience with frameworks like dbt or SQLMesh, mentioning what you find appealing about their features and community support. Support your answer with examples of projects you've completed using these tools.

Illustrate your approach to understanding stakeholder needs, possibly including a collaborative process of defining key performance indicators (KPIs). Share successful experiences where you translated business objectives into specific metrics.

Be prepared to detail your proficiency with data warehouses like Redshift, Snowflake, or BigQuery, touching on your familiarity with their structures, optimal querying approaches, and scenarios where you’ve implemented or improved warehousing strategies.

Explain your experience with techniques such as indexing, partitioning, and normalizing tables. Include real instances where these optimizations led to measurable improvements in performance or cost efficiency.

Talk about your commitment to maintaining clear and precise documentation for all data processes. Offer examples of documentation practices you’ve implemented, emphasizing their importance for onboarding and ongoing maintenance.